Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework

• URL: http://arxiv.org/abs/2508.14940v2
• Date: Tue, 26 Aug 2025 17:59:53 GMT
• Title: Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework
• Authors: Chongyu Qu, Allen J. Luna, Thomas Z. Li, Junchao Zhu, Junlin Guo, Juming Xiong, Kim L. Sandler, Bennett A. Landman, Yuankai Huo,
• Abstract summary: Lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings.<n>We propose a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient.
• Score: 4.828586430285072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all cohorts. To address this, we propose a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient by combining cohort-specific knowledge with modern retrieval and reasoning techniques. Given a patient's CT scan and structured metadata -- including demographic, clinical, and nodule-level features -- the agent first performs cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts to identify the most relevant patient population from a multi-institutional database. Second, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TD-VIT, DLSTM), and multi-modal computer vision-based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline -- retrieval via FAISS and reasoning via LLM -- enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.

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